PP: Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data
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From: Stanford University; Jure Leskovec, citation 6w+;
Problem:
subsequence clustering.
Challenging:
discover patterns is challenging because it requires simultaneous segmentation and clustering of the time series + interpreting the cluster results is difficult.
Why discover time series patterns is a challenge?? thinking by yourself!! there are already so many distance measures(DTW, manifold distance) and clustering methods(knn,k-means etc.). But I admit the interpretation is difficult.
Introduction:
long time series ----breakdown-----> a sequence of states/patterns ------> so time series can be expressed as a sequential timeline of a few key states. -------> discover repeated patterns/ understand trends/ detect anomalies/ better interpret large and high-dimensional datasets.
Key steps: simultaneously segment and cluster the time series.
Unsupervised learning: hard to interpretation, after clustering, you have to view data itself.
how to discover interpretable structure in the data?
distance-based metrics, DTW.
Reference:
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